ARR · 106

Whoever sings down Anthropic may be disappointed

Author: Alan Walker, Silicon Valley Original title: Is Anthropic's Growth Slowing Down? Source of controversy. Claude Code ARR tracking chart produced by TickerTrends. The latest data is $15.12 billion for the week of August 10, 2026, accounting for 21.9% of Anthropic's total ARR. Please note: This is an estimate from a third party agency and is not an official disclosure of Anthropic. The first section below explains how important this difference is. Alan Walker from Silicon Valley made an appointment for dinner in Hong Kong. After some hard work, he discovered that this picture had been retweeted more than 30 times, and the matching statement was similar — “Anthropic's growth has leveled off; 2 trillion dollars is a bubble.” Alan saved the image, zoomed it in, and looked at it again. The problem isn't in this picture. This picture is very well done, and the data is probably done seriously. The problem is that almost everyone who retweeted it was using it to answer a question it couldn't answer at all. 01 Let's first figure out who made this picture, there is a Claude icon in the upper left corner. The color scheme is Claude's familiar orange. At first glance, it looks like an official product. It's not. The author of this picture is TickerTrends and has his name written in the upper right corner. It is a third-party data tracking agency that uses various external signals (application data, payment panels, recruitment, channel caliber, etc.) to estimate the revenue of an unlisted company. The line in the picture is written very honestly: “tracked allocation” -- the percentage of allocations that have been tracked. Let's be clear: Anthropic has never publicly disclosed Claude Code's individual ARR numbers, not once. Every point on this curve has been estimated by an outsider. For example, this is like someone using “long queues at the entrance of a restaurant every day” to estimate its turnover and then draw a beautiful weekly curve. The length of the team does correlate with turnover, but in the middle there is turnover rate, customer unit price, takeout ratio, private room business — you see that the team is three short weeks, and the kitchen is probably being renovated in those three weeks. What is more important is the caliber itself. ARR's algorithm is “revenue for the most recent period times 12.” Enterprise software contracts are not executed evenly every day; they are signed batch by batch. Big orders signed at the end of a quarter will jump a week's curve by a large margin; if the next quarter's big orders aren't signed, the curve will go sideways. Weekly ARR tracking is extremely insensitive to this kind of blocky landing—it will paint the “pace of signing” as a “change in demand.” In a nutshell, what you have in your hand is an unofficial weekly map estimated by an outsider, with a very blunt caliber. Judging by the weight of the “bubble” under it is tantamount to using body temperature to measure blood pressure. 02 I hit myself in the face on this picture. I haven't seen anyone mention it, but it's the most interesting part of the whole thing. The picture shows two numbers: Claude Code is $15.12 billion, or 21.9% of Anthropic's total ARR. By dividing: calculate 15.12 billion ÷ 21.9% = about $69 billion. This is Anthropic's total ARR for the week ending August 10, implied by this image. The official caliber figures reported by Bloomberg, Reuters, and CNBC on August 17 were — $65 billion at the end of July. Clear: This chart, which is being used to prove “slowing growth,” its own implied total number of companies is 4 billion US dollars higher than the official figure ten days ago. Further 10 days until today, if the trend continues, more than 70 billion is a reasonable estimate (this sentence is an inference, not data). In one sentence, people who retweeted only read the number 151.2 and the height of the column, skipping the 21.9% next to it. And that 21.9% said: This company went a step further when everyone shouted “it's slowing down.” I only believe in the two numbers on the same picture that is beneficial to my opinion; this is not called analysis. 03 You are looking at the picture below. The money in the picture above has the upper and lower two pieces. Above is the absolute amount (how many billion dollars), and below is the percentage change (how much more than a percent increase from four weeks ago). The vast majority of people's reasoning is: below...

1d agoWendy#Anthropic #ARR #IPOs #MiniMax

Alibaba's Wu Yongming: Ali AI's annualized revenue exceeds 49.5 billion yuan

Comparing news, Alibaba announced that Alibaba Cloud is undergoing a full upgrade to an intelligent cloud. In the next few quarters, AI and cloud business revenue growth will further accelerate. Alibaba Group released financial results for the first quarter of the 2027 fiscal year. During the analysts' conference call, Group CEO Wu Yongming said that AI has become the core engine for Alibaba Cloud's accelerated growth. This quarter, Ali's AI-related product annualized revenue (ARR) surpassed 49.5 billion yuan ($7.3 billion), and its share of Alibaba Cloud's external commercial revenue rose to 35%. The gross margin of AI-related products is significantly higher than the average for cloud products.

2d ago

Anthropic 40% of ARR is sold by cloud vendors: $65 billion in annualized revenue is not that easy to earn

Comparative news, AI news, Anthropic's annualized revenue just reached $65 billion, and SemiAnalysis then split this revenue. According to its model estimates, more than 40% of ARR in the second quarter came from indirect channels such as AWS Bedrock, Microsoft Foundry, and Google's enterprise AI platform. What is worth paying attention to is how much profit these revenues can leave behind. In Bedrock, for example, Claude was sold by Anthropic. Anthropic will count the total amount of tokens sold into ARR, and then pay AWS for computing power and channel sharing. In other words, it's also a $1 ARR. If you sell it through a cloud platform, Anthropic will end up leaving less money than direct sales. So while $65 billion ARR isn't fake income, the revenue structure is clearly not that healthy. The higher the share of channels, the less direct equations between revenue growth and profit growth. If you only look at ARR, you might be overestimating the contribution of these revenues to Anthropic's final profit. Of course, there are benefits to the channel model. AWS, Microsoft, and Google already have a large number of enterprise customers and procurement contracts, and can directly cram Claude into existing cloud bills. Anthropic is now trading some of its profits for scale and customer acquisition efficiency.

3d ago

SemiAnalysis: Anthropic Q2 over 40% ARR comes from indirect channels

In comparison, according to SemiAnalysis, Anthropic's ARR from indirect channels (Bedrock, Foundry, Gemini Agent Enterprise) in Q2 2026 accounted for more than 40%, and API and B2B contributed most of the new revenue. Indirect channels differ from direct revenue monetization models. Cloud service providers usually charge IaaS fees or revenue shares. Laboratories include gross ARR, but related costs are reflected in sales and marketing expenses, and indirect channels require computing power allocation analysis accurate to the level of a single transaction.

3d ago

Why is capital chasing AI Native and ignoring the old Internet

Capital doesn't reward being old-fashioned, not because old-fashioned people are at fault. The old part is clearly priced. There is no bad information, so there is no excess profit. Global venture capital was $510 billion in the first half of 2026, surpassing $44 billion for the full year of 2025 in one and a half months. More than 70% have entered AI; OpenAI and Anthropic took 217 billion dollars, accounting for 43%. With that much money, you'd think everyone could share a little bit. The truth is that distribution is more extreme than total volume, and the first sieve doesn't screen the industry, it screens people. The category that has been screened out now has an unkind name: the internet is old. Let's just say one thing: the “old man” in this article has nothing to do with age. It refers to a set of methodologies that have been formed in the mobile internet cycle, have been tested over and over, and have brought huge returns to holders. The person holding it may be 45 years old or 32 years old. It was this methodology that was being repriced, not the year of birth. Confusing these two things is Lao Deng's most common mistake and one of the most comfortable mistakes — because if the problem is someone else's age discrimination, you don't need to change a single word. 01 What is AI Native The term has been misused. They can use ChatGPT not called AI native, nor AI in the company name, let alone in their twenties. There are three things that really separate people. First, the starting point is a model, not a requirement. The order in which Lao Deng makes a product is: look at what the user wants, write down the requirements, and find technology to implement it. The order of AI natives is reversed: first figure out what level the model is capable of today and what step it is likely to reach tomorrow, and then move from this capability boundary to the external product. The former uses the model as a tool, and the latter uses the model as the foundation. There was no difference between these two kinds of things made by humans in the first edition; by the third edition, there was a difference of one species. Article 2. The default unit of an organization is not a person. The division of labor in the Internet age is the division of one thing into ten people. AI Native's division of labor is to take ten things from one person and add a bunch of agents. The CEO of a domestic application company said that the team consists of less than ten people, but a large number of AI work at night, and the first thing employees do every morning is check the work the AI handed in the night before. Cursor's side is even more extreme. Public reports mention that the company doesn't have a product manager; engineers write their own code, talk to users themselves, and participate in recruiting people themselves. Article 3. Information is first-hand. AI Native's input sources are papers, model cards, GitHub issues, original discussions on X, and self-run evals. Lao Deng's input sources are industry summits, closed-door meetings, brokerage reports, interpretation of public accounts, and finding someone to drink coffee with. This one is the least obscure and most lethal; I'll talk about that separately later. I'm satisfied with all three. The 25-year-old is an AI native, and so is the 45-year-old. I'm not satisfied with the three rules; I'm still an old man at the age of 25. AI natives are a state, not an age group. The trouble is that tickets in this state are works, not resumes. 02 The two lists spread the results of this round on the table. These are two lists. The first one is an all-AI native company. Their valuations are not rising; they are exchanging orders of magnitude. List 1 · Upstream OpenAI raised $122 billion in a single round of financing in Q1 2026, followed by $852 billion, the largest private equity financing in history. Anthropic Q2 had a single round of $65 billion, after investing $965 billion, accounting for about half of the total global venture capital for the quarter; the revenue operating rate in May reached about $47 billion. DeepSeek raised about 70 billion yuan in its first round of financing in May 2026. In April of the same year, Liang Wenfeng raised his direct shareholding from 1% to 34%, and controlled a total of about 84.29% of the shares through related entities. The Dark Side of the Moon (Kimi) was estimated at $4.3 billion in December 2025; it went for three consecutive rounds from January to February 2026 to reach 18 billion; the D round in May was about $2 billion, breaking 20 billion dollars after the investment; the July round surpassed $3.5 billion, after investing 35 billion dollars; the pre-IPO target was 50 billion dollars. ARR broke 100 million in March, 200 million in May, and held steady at 300 million US dollars in June, with APIs accounting for more than 70%. Smart Spectrum · MiniMax successively landed in Hong Kong stocks in early 2026, with a market capitalization exceeding 100 billion yuan. It was one of the first major model companies listed in China. The second one...

3d agoWendy#AI #DeepSeek

Since Elliwa called for AGPU, the company's stock price has risen by more than 50%

Comparatively, since Liquid Capital (former LD Capital) founder E-Lihua was first optimistic about Axe Compute (NASDAQ: AGPU) on July 27, the company's stock price has risen by more than 50%, and its market capitalization has risen to $123 million. At first, E-Lihua thought that the most definitive alpha opportunity on the computing power circuit was often hidden in extreme valuation differences! The US stock AGPU recently won more than 1.6 billion US dollars in contracts. The forward ARR reached 384 million, but the market capitalization was less than 100 million, and the P/ARR is only 0.2 times! Compared to CRWV (market capitalization of 50 billion dollars), which is heavily asset-heavy and heavily in losses, AGPU's asset-light Access+ exclusive Build hybrid model is simply a dimensional reduction blow. Afterwards, another article was published today stating that the company was seriously undervalued, and the Q2 earnings report released four major benefits.

3d ago

Fireworks that came out of Meta to talk about open source and closed source. Who will win?

Author: Silicon Valley Vector Silicon Valley Coordinates Editor: Peggy, BlockBeats Original title: Silicon Valley Coordinates x Fireworks Co-Founder Benny Chen: Open Source Models, Token Growth, Inference Optimization, and Model Customization Editor's Note: In the context of open source models speeding up and approaching cutting-edge closed-source models, industry discussions are shifting from “who has the most capable model” to “who can put models into production at a lower cost”. However, when model capabilities converged and token consumption increased, a lower-level question began to emerge: are companies really willing to pay a cheaper model call, or exclusive intelligence that can perform specific tasks in a stable manner? Recently, Cao Qingyun, host of “Silicon Valley Coordinates”, had a conversation with Chen Yufei, co-founder of Fireworks AI. Located between models and enterprise applications, Fireworks mainly provides customers with open source model inference, performance optimization, and customization services. Rather than simply discussing whether open source can catch up with closed sources, Chen Yufei's observations are closer to actual workloads: where tokens flow, why companies pay, and what is still missing from the model from proof of concept to production. In this conversation, Chen Yufei disassembled “who wins between open source and closed source” into a set of lower level structural questions: can token growth be converted into revenue, can generic capabilities replace vertical accumulation, can the low price model pass corporate evaluation, and how the inference platform can gain value between cloud vendors and application companies. First, the scale of use and commercial value of the open source model are diverging. In the past, the ability to catch up and call price were the main indicators for judging the competitiveness of open source; today, the Fireworks platform processes about 40 trillion to 50 trillion tokens every day, and the actual usage of the open source model has rapidly expanded. However, free traffic, promotional subsidies, and model price differences will cause Token statistics to overestimate some demand. Customers may heavily use lower-cost models and still hand over the highest budget to the best-performing closed source model. This means that the next phase of open source is not just expanding traffic, but proving that it can meet or even surpass cutting-edge models for high-value tasks, and turn cost advantages into willingness to pay. Second, the general model and the vertical model are beginning to evolve in different directions. In the past, every time a cutting-edge model was upgraded, it was possible to directly eliminate a number of fine-tuned models; now, vertical applications such as law, medical care, and programming are accumulating more detailed evaluations, data, and workflows, and their optimization goals are gradually separated from cutting-edge laboratories. Generic models need to increase the upper limit of capabilities, while vertical models require stable delivery of results in limited scenarios. The former can solve a wider range of problems, while the latter has a better understanding of how users define “right.” This means that the barrier for vertical companies is not just having a customized model, but being able to continuously transform industry needs into an evaluation system and migrate over and over again as the basic model is updated. Third, the bottleneck in enterprise AI implementation is shifting from model supply to evaluation capabilities. In the past, enterprise proof of concept often relied on trial experience and subjective judgment; now, when AI enters production processes such as call centers, legal searches, and medical assistance, it is no longer possible to support procurement decisions simply by “looking good.” Businesses must know what tasks the model works for, when it fails, and how much the cost and quality of switching from closed source to open source changes. Assessment is therefore no longer an ancillary tool, but an infrastructure connecting procurement, training, and production deployment. Who can define tasks, establish test distributions, and continuously update standards can truly control model choices. Fourth, the value of inference platforms is shifting from “selling cheap computing power” to organizational models, hardware, and workflows. In the past, inference optimization was mainly understood to reduce the cost of a single token; now, caching, task splitting, model routing, and context management can all directly change the task completion rate. Different models don't have to compete for the same position; they can act as performers and advisors separately. Fireworks' business logic is also based on this: instead of building asset-heavy hardware, revenue is tied to actual use of customer models through training, customization, and continuous reasoning. But the main rival in this path is not a single new cloud company, but a large cloud vendor that can simultaneously control computing power, software, and customer portals. Fifth, the rise of the open source model may not reduce infrastructure requirements; on the contrary, it may reduce model layer premiums and further push value towards reasoning and computing power. Tech giants continue to increase capital spending, not just calculating short-term returns, but measuring missed AI cycles...

3d ago律动BlockBeats#AI

Anthropic ARR sparks valuation controversy: behind $65 billion in annualized revenue, the market is questioning AI's growth slope

Comparing news, Anthropic's revenue growth is still entering the capital market at an accelerated pace, but controversy surrounding its valuation is also heating up. Bloomberg previously reported that Anthropic had an annualized revenue operating rate of around $65 billion as of the end of July. This figure is still astonishing on the surface, but the focus of market discussions has turned to another level: whether $65 billion means that the growth slope is slowing against the backdrop of some third-party data and optimistic expectations in the AI community pointing at more than $80 billion. The controversy first stemmed from the ARR caliber. ARR, or annual recurring revenue, essentially annualizes the current revenue rate and is not equivalent to audited annual revenue. According to Sacra data, Anthropic's annualized revenue in May was about 47 billion US dollars and rose to 65 billion US dollars in July, but it also warned that revenue from cloud channels such as AWS, Google, and Microsoft may be confirmed in terms of total volume, which will make the scale of revenue seem larger and will also raise the market's focus on gross margin and revenue quality. Optimists still believe that this number is sufficient proof of the strong demand for AI in enterprises. Gavin Baker of Atreides Management believes that Anthropic has had an advantage over OpenAI in terms of token efficiency; PitchBook's Harrison Rolfes points out that even if the model price is higher, if the task success rate is higher, enterprise customers may still accept a higher unit price. In other words, what many people value is not simply the size of API calls, but Claude's ability to pay in the enterprise workflow. Cautists, on the other hand, believe that the market will need to wait for the IPO prospectus to be verified. Simon Willison previously pointed out that run-rate revenue usually comes from short-term annualization of income and cannot be directly regarded as full-year accounting revenue. ThinkFast's Ken Koo also warned that what really matters will be the audit revenue, gross profit margin, customer concentration, computing power procurement obligations, and cash flow in the S-1 file. Ed Zitron more directly questioned that AI companies' ARR may be affected by fluctuations in prepaid tokens, cloud channel revenue, and short-term usage. This discussion isn't just about Anthropic's valuation. Steve Eisman previously referred to OpenAI and Anthropic as key risk points in AI transactions because capital expenses and cloud revenue expectations of tech giants such as Microsoft, Amazon, Google, and Oracle are increasingly tied to leading AI labs.

4d ago

[Comparative Daily News Picks] Yushu Technology will be listed on the Science and Technology Innovation Board on August 19; Anthropic's annualized revenue exceeded 65 billion US dollars before the IPO; Ethereum developers plan to upgrade Hegotá's priority promotion of private transaction proposals in 2027, and FOCIL has confirmed inclusion; the US-Iran situation has added another variable, and the yield on 30-year US bonds hit a new high in 19 years

Daily AI · Crypto · Macro · Market News, Bitpush helps you set priorities ↓ AI · News [Yushu Technology will be listed on the Science and Technology Innovation Board on August 19]. Comparing news, Yushu Technology announced that the company's shares will be listed on the Science and Technology Innovation Board of the Shanghai Stock Exchange on August 19, 2026. [Anthropic's annualized revenue surpassed 65 billion US dollars before the IPO] In comparison, according to people familiar with the matter, Anthropic's current performance means that the company's annualized revenue is expected to exceed 65 billion US dollars, an increase of more than seven times from the level at the end of last year. As of the end of July, Anthropic's annual recurring revenue (ARR) had reached $65 billion, according to people familiar with the matter. One of the people familiar with the matter said that Anthropic shared this data when regularly updating investors on the company's situation. The sharp acceleration in revenue has further strengthened Anthropic's confidence in advancing its listing plan. Both Anthropic and OpenAI have secretly submitted documents related to the listing. Anthropic is expected to land on Wall Street as soon as this fall, possibly earlier than OpenAI. [OpenAI Super Data Center officially launched, Nvidia covered up to 105 billion US dollars] Comparing news, OpenAI's Ohio Super Data Center officially signed a contract. This project was previously revealed. At the time, OpenAI was still discussing a long-term lease with SB Energy, and Nvidia was only considering providing a guarantee. Now that the first 4.25 GW has been officially launched, Nvidia can continue to lock in the remaining 3.75 GW. Previously, the two sides discussed guarantees of up to 250 billion US dollars, and in the end, the initial liability was limited to 105 billion US dollars. This isn't money given directly to OpenAI. Only if OpenAI goes bankrupt or doesn't pay rent, and there is still a gap after the project is re-leased or sold, will Nvidia need to make up the difference. After that, OpenAI will also have to pay back the money actually advanced by Nvidia. Nvidia will also invest $1.5 billion in developer SB Energy, and the park will mainly use Nvidia's AI computing power. Hwang In-hoon estimates that each generation of systems deployed here may correspond to about 1.5 million GPUs and 150 billion to 200 billion US dollars in revenue. Until now, outsiders have been questioning that this model is circular financing: Nvidia backs up the customer's infrastructure, and the customer then uses the money to buy Nvidia chips. Hwang In-hoon also specifically responded this time, stressing that Nvidia only bears specific rent, electricity, and asset residual value risks; it is not responsible for the entire project on behalf of OpenAI. Crypto · Market [Ethereum developers plan to prioritize private transaction proposals in the 2027 Hegotá upgrade, FOCIL has confirmed inclusion] In comparison, Ethereum Foundation researcher Toni Wahrstätter said that the protocol architecture team hopes to prioritize Frame Transactions (EIP-8141) and FOCIL (EIP-7805) in the Hegotá upgrade planned for 2027. Frame Transactions can collaborate with Keyed Nonces and Recent Roots (EIP-8272) and Transaction Assertions (EIP-7906) to enable the privacy pool to pay transaction fees and let the wallet set execution conditions after transaction submission. FOCIL can provide agreement layer inclusion guarantees for eligible transactions. Currently, FOCIL is the only proposal that Hegotá has confirmed inclusion, and the Frame Transactions related scheme is one of 66 proposals currently being evaluated. Other candidate solutions include transaction pricing, status growth, block access lists, and optional zkEVM certification for the main network. Vitalik Buterin previously proposed improving Ethereum's privacy, resisting quantum security, and reducing reliance on second-layer networks. Hegotá will follow Glamsterdam, and the developers plan to complete Glamsterdam by the end of 2026. [CleanSpark, BitFufu, and Canan Technology's Bitcoin production in July fell by about 5%, 10%, and 28%, respectively] In comparison news, according to The Block, Bitcoin mining companies CleanSpark, BitF...

4d agoBitpushNews#Compare Daily Picks

Anthropic's annualized revenue surpassed $65 billion before IPO

Comparative news, according to people familiar with the matter, Anthropic's current performance means that the company's annualized revenue is expected to exceed 65 billion US dollars, an increase of more than seven times over the level at the end of last year. As of the end of July, Anthropic's annual recurring revenue (ARR) had reached $65 billion, according to people familiar with the matter. One of the people familiar with the matter said that Anthropic shared this data when regularly updating investors on the company's situation. The sharp acceleration in revenue has further strengthened Anthropic's confidence in advancing its listing plan. Both Anthropic and OpenAI have secretly submitted documents related to the listing. Anthropic is expected to land on Wall Street as soon as this fall, possibly earlier than OpenAI.

4d agoWendy